Fully tuned RBF neural network controller for ultrasound hyperthermia cancer tumour therapy.

Thermal dose is an important clinical efficacy index for hyperthermia cancer treatment. This paper presents a new direct radial basis function (RBF) neural network controller for high-temperature hyperthermia thermal dose during the therapeutic procedure of cancer tumours by short-time pulses of high-intensity focused ultrasound (HIFU). The developed controller is stabilized and automatically tuned based on Lyapunov functions and ant colony optimization (ACO) algorithm, respectively. In addition, this thermal dose control system has been validated using one-dimensional (1-D) biothermal tissue model. Simulation results showed that the fully tuned RBF neural network controller outperforms other controllers in the previous studies by achieving targeted thermal dose with shortest treatment times less than 13.5 min, avoiding the tissue cavitation during the thermal therapy. Moreover, the maximum value of its mean integral time absolute error (MTAE) is 98.64, which is significantly less than the resulted errors for the manual-tuned controller under the same treatment conditions of all tested cases. In this study, integrated ACO method with robust RBF neural network controller provides a successful and improved performance to deliver accurate thermal dose of hyperthermia cancer tumour treatment using the focused ultrasound transducer without external cooling effect.

Aortic valve prosthesis tracking for transapical aortic valve implantation.

PURPOSE: Transapical aortic valve implantation (TA-AVI) is a new minimally invasive surgical treatment of aortic stenosis for high-risk patients. The placement of aortic valve prosthesis (AVP) is performed under 2D X-ray fluoroscopic guidance. Difficult clinical complications can arise if the implanted valve is misplaced. Therefore, we present a method to track the AVP in 2D X-ray fluoroscopic images in order to improve the accuracy of the TA-AVI. METHODS: The proposed tracking method includes the template matching approach to estimate the position of AVP and a shape model of the prosthesis to extract the corner points of the AVP in each image of sequence. To start the AVP tracking procedure, an initialization step is performed by manually defining the corner points of the prosthesis in the first image of sequence to provide the required algorithm parameters such as the AVP model parameters. RESULTS: We evaluated the AVP tracking method on six 2D intra-operative fluoroscopic image sequences. The results of automatic AVP localization agree well with manually defined AVP positions. The maximum localization errors of tracked prosthesis are less than 1 mm and within the clinical accepted range. CONCLUSIONS: For assisting the TA-AVI, a method for tracking the AVP in 2D X-ray fluoroscopic image sequences has been developed. Our AVP tracking method is a first step toward automatic optimal placement of the AVP during the TA-AVI.

Towards a new image guidance system for assisting transapical minimally invasive aortic valve implantation.

We propose a new image guidance system for assisting transapical minimally invasive aortic valve implantation. The goal is to define the exact positioning of aortic valve prosthesis, preventing the misplacement of the valve. The proposed system consists of two stand-alone modules. First, preoperative planning software uses DynaCT images with manual anatomical landmarks to calculate the size and optimal position of the prosthesis. Second, an intraoperative system is developed for tracking of the prosthesis and the coronary ostia in 2-D fluoroscopic images. Then the safe area of implantation is defined. The preliminary experimental results of preoperative planning and intraoperative tracking system are promising.